Multiomics Profiling of Plasma Reveals Molecular Alterations Prior to a Diagnosis with Stroke Among Chinese Hypertension Patients

We aimed to investigate the correlation between plasma proteins and metabolites and the occurrence of future strokes using mass spectrometry and bioinformatics as well as to identify other biomarkers that could predict stroke risk in hypertensive patients. In a nested case-control study, baseline plasma samples were collected from 50 hypertensive subjects who developed stroke and 50 gender-, age- and body mass index-matched controls. Plasma untargeted metabolomics and data independent acquisition-based proteomics analysis were performed in hypertensive patients, and 19 metabolites and 111 proteins were found to be differentially expressed. Integrative analyses revealed that molecular changes in plasma indicated dysregulation of protein digestion and absorption, salivary secretion, and regulation of actin cytoskeleton, along with significant metabolic suppression. C4BPA, Caprolactam, Col15A1, and HBB were identified as predictors of stroke occurrence, and the Support Vector Machines (SVM) model was determined to be the optimal predictive model by integrating six machine-learning classification models. The SVM model showed strong performance in both the internal validation set (area under the curve [AUC]: 0.977, 95% confidence interval [CI]: 0.941-1.000) and the external independent validation set (AUC: 0.973, 95% CI: 0.921-0.999).